Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls

Simon, Gyorgy J., Aliferis, Constantin

Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls
  • 出版商: Springer
  • 出版日期: 2024-03-30
  • 售價: $2,210
  • 貴賓價: 9.5 折 $2,100
  • 語言: 英文
  • 頁數: 810
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 3031393570
  • ISBN-13: 9783031393570
  • 相關分類: 人工智慧、Machine Learning

    • 海外代購書籍(需單獨結帳)

商品描述

This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks.

Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls is a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.

商品描述(中文翻譯)

這本開放存取的書籍詳細回顧了人工智慧（AI）和機器學習（ML）在醫學領域中的最新方法和應用。書中的章節專注於讓讀者對這些主題的關鍵概念有深入的理解，並介紹一系列可用於解決醫療問題的方法和模型，其中包括因果和預測模型的全面討論。書中系統地描述了這些概念，以幫助讀者深入理解不同方法和模型的運作方式，以及它們與醫療保健和醫學科學中各種問題的適用性之間的關係。書中還提供了如何避免日常遇到的問題和分層潛在臨床風險的指導。

《人工智慧和機器學習在醫療保健和醫學科學中的最佳實踐和陷阱》是一本全面指南，介紹了如何最好地應用AI和ML技術在醫療保健中。書中強調如何避免各種可能遇到的問題，使其成為所有醫學信息學專業人員和日常使用這些方法的醫生的必備指南。此外，這本書對健康數據科學家、管理人員以及醫學科學領域的學生也具有重要的參考價值，提供了一個關於這一主題的最新資源。

作者簡介

Dr. Gyorgy Simon earned his PhD in Computer Science with a minor in Statistics from the University of Minnesota. Subsequently, he was a senior software engineer at Yahoo! Search Engine Technologies and later joined Mayo Clinic where he developed clinical data mining techniques, before joining the University of Minnesota He is a federally-funded investigator with extensive experience developing and applying AI and ML methods in a variety of application settings. He is currently a tenured Associate Professsor in the Institute for Health Informatics with additional appointments in Medicine and Data Science.

Constantin Aliferis received an MD degree from Athens University in Greece in 1990, and an MS in 1994 and PhD in 1998 in Artificial Intelligence from the University of Pittsburgh. He also completed a post doctoral Fellowhip focusing on Machine Learning in Biomedicine. His has served as faculty in Biomedical Informatics, Computer Science, Biostatistics and Cancer Biology at Vanderbilt University; Informatics, Computational Biology, Data Science and Pathology at NYU; and Informatics, Medicine and Data Science at the University of Minnesota. He has also been a regular faculty member in the Cancer Centers of the above universities and architected/led their MS and PhD programs in Biomedical Informatics. He has also been the director of the NYU's Center for Health Informatics and Bioinformatics, and Director of the UMN Institute for Health Informatics at the UMN where he is also Chief Research Informatics Officer and a tenured Professor. He is a federally-funded investigator who has pioneered several novel and best-of-breed AI and ML methods, applied them in dozens of areas, and has also published extensively in method benchmarking and several other best-practice-related topics.

作者簡介(中文翻譯)

Dr. Gyorgy Simon在明尼蘇達大學獲得計算機科學博士學位，副修統計學。隨後，他在Yahoo! Search Engine Technologies擔任高級軟體工程師，後來加入Mayo Clinic，在那裡他開發了臨床數據挖掘技術，然後加入明尼蘇達大學。他是一位獲得聯邦資助的研究者，具有在各種應用領域開發和應用人工智慧和機器學習方法的豐富經驗。他目前是健康資訊學院的終身副教授，並在醫學和數據科學領域擔任其他職位。

Constantin Aliferis於1990年在希臘雅典大學獲得醫學博士學位，並於1994年和1998年在匹茲堡大學獲得人工智慧碩士和博士學位。他還完成了一個關注生物醫學中機器學習的博士後研究。他曾在范德堡大學的生物醫學信息學、計算機科學、生物統計學和癌症生物學學院擔任教職；在紐約大學的信息學、計算生物學、數據科學和病理學學院擔任教職；以及在明尼蘇達大學的信息學、醫學和數據科學學院擔任教職。他還是上述大學的癌症中心的常任教職成員，並設計/領導了他們的生物醫學信息學碩士和博士學位課程。他還曾擔任紐約大學健康信息學和生物信息學中心的主任，以及明尼蘇達大學健康資訊學院的主任，同時也是首席研究信息官和終身教授。他是一位獲得聯邦資助的研究者，開創了幾種新穎且最佳的人工智慧和機器學習方法，在許多領域應用了這些方法，並在方法基準測試和其他最佳實踐相關主題上發表了大量論文。

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